import pandas as pd
import numpy as np

data = pd.read_csv("data.csv")

print("Top 10 rows :\n")
data.head(10)

Top 10 rows :

Total_Rows = data.shape[0]
Total_Cols = data.shape[1]
print("Total Rows is :",Total_Rows)
print("Total columns is :", Total_Cols)

Total Rows is : 569
Total columns is : 33

print( "\n Information about the Titanic dataset : \n " )
data.info()

 Information about the Titanic dataset : 
 
<class 'pandas.core.frame.DataFrame'>
RangeIndex: 569 entries, 0 to 568
Data columns (total 33 columns):
 #   Column                   Non-Null Count  Dtype  
---  ------                   --------------  -----  
 0   id                       569 non-null    int64  
 1   diagnosis                569 non-null    object 
 2   radius_mean              569 non-null    float64
 3   texture_mean             569 non-null    float64
 4   perimeter_mean           569 non-null    float64
 5   area_mean                569 non-null    float64
 6   smoothness_mean          569 non-null    float64
 7   compactness_mean         569 non-null    float64
 8   concavity_mean           569 non-null    float64
 9   concave points_mean      569 non-null    float64
 10  symmetry_mean            569 non-null    float64
 11  fractal_dimension_mean   569 non-null    float64
 12  radius_se                569 non-null    float64
 13  texture_se               569 non-null    float64
 14  perimeter_se             569 non-null    float64
 15  area_se                  569 non-null    float64
 16  smoothness_se            569 non-null    float64
 17  compactness_se           569 non-null    float64
 18  concavity_se             569 non-null    float64
 19  concave points_se        569 non-null    float64
 20  symmetry_se              569 non-null    float64
 21  fractal_dimension_se     569 non-null    float64
 22  radius_worst             569 non-null    float64
 23  texture_worst            569 non-null    float64
 24  perimeter_worst          569 non-null    float64
 25  area_worst               569 non-null    float64
 26  smoothness_worst         569 non-null    float64
 27  compactness_worst        569 non-null    float64
 28  concavity_worst          569 non-null    float64
 29  concave points_worst     569 non-null    float64
 30  symmetry_worst           569 non-null    float64
 31  fractal_dimension_worst  569 non-null    float64
 32  Unnamed: 32              0 non-null      float64
dtypes: float64(31), int64(1), object(1)
memory usage: 146.8+ KB

print("duplicate values is : ",data.duplicated().sum())

duplicate values is :  0

print("Total columns is here :\n", data.columns.tolist())

Total columns is here :
 ['id', 'diagnosis', 'radius_mean', 'texture_mean', 'perimeter_mean', 'area_mean', 'smoothness_mean', 'compactness_mean', 'concavity_mean', 'concave points_mean', 'symmetry_mean', 'fractal_dimension_mean', 'radius_se', 'texture_se', 'perimeter_se', 'area_se', 'smoothness_se', 'compactness_se', 'concavity_se', 'concave points_se', 'symmetry_se', 'fractal_dimension_se', 'radius_worst', 'texture_worst', 'perimeter_worst', 'area_worst', 'smoothness_worst', 'compactness_worst', 'concavity_worst', 'concave points_worst', 'symmetry_worst', 'fractal_dimension_worst', 'Unnamed: 32']

numerical_col = data.select_dtypes(include=["int64","float64"]).columns
print("Total Numerical columns list is here :\n", numerical_col)
print("\nTotal Numerical columns is here :\n" ,numerical_col.value_counts().sum())

Total Numerical columns list is here :
 Index(['id', 'radius_mean', 'texture_mean', 'perimeter_mean', 'area_mean',
       'smoothness_mean', 'compactness_mean', 'concavity_mean',
       'concave points_mean', 'symmetry_mean', 'fractal_dimension_mean',
       'radius_se', 'texture_se', 'perimeter_se', 'area_se', 'smoothness_se',
       'compactness_se', 'concavity_se', 'concave points_se', 'symmetry_se',
       'fractal_dimension_se', 'radius_worst', 'texture_worst',
       'perimeter_worst', 'area_worst', 'smoothness_worst',
       'compactness_worst', 'concavity_worst', 'concave points_worst',
       'symmetry_worst', 'fractal_dimension_worst', 'Unnamed: 32'],
      dtype='object')

Total Numerical columns is here :
 32

categorical_col = data.select_dtypes(include=["O"]).columns
print("Total categorical columns  list is here :\n", categorical_col)
print("\nTotal categorical columns is here :\n" ,categorical_col.value_counts().sum())

Total categorical columns  list is here :
 Index(['diagnosis'], dtype='object')

Total categorical columns is here :
 1

data.describe().T

data.describe(include="all").T

data.describe(include="O").T

import matplotlib.pyplot as plt
import seaborn as sns

data.hist(numerical_col, figsize=(40,20))

plt.ylabel("Frequency")
plt.tight_layout()
plt.show()

sns.pairplot(data)
plt.tight_layout()
plt.show()

correlation = data.corr(numeric_only=True)

plt.figure(figsize=(43,35))
sns.heatmap(correlation , annot=True  ,cmap="coolwarm", fmt=".2f")
plt.title("Correlation Metrix")
plt.tight_layout()
plt.show()

for col in data:
    plt.figure(figsize=(6,2))
    sns.countplot(x=data[col],color="gold")
    plt.title(f"Boxplot of {col}")
    plt.show()

data.isna().sum()

id                           0
diagnosis                    0
radius_mean                  0
texture_mean                 0
perimeter_mean               0
area_mean                    0
smoothness_mean              0
compactness_mean             0
concavity_mean               0
concave points_mean          0
symmetry_mean                0
fractal_dimension_mean       0
radius_se                    0
texture_se                   0
perimeter_se                 0
area_se                      0
smoothness_se                0
compactness_se               0
concavity_se                 0
concave points_se            0
symmetry_se                  0
fractal_dimension_se         0
radius_worst                 0
texture_worst                0
perimeter_worst              0
area_worst                   0
smoothness_worst             0
compactness_worst            0
concavity_worst              0
concave points_worst         0
symmetry_worst               0
fractal_dimension_worst      0
Unnamed: 32                569
dtype: int64

#removing last column because it contains no meaningful Value
data = data.drop("Unnamed: 32",axis=1)

categorical_col

Index(['diagnosis'], dtype='object')

from sklearn.preprocessing import LabelEncoder

lb = LabelEncoder()
data.diagnosis = lb.fit_transform(data.diagnosis)
data

for i in data:
    q1 = data[i].quantile(0.25)
    q3 = data[i].quantile(0.75)
    iqr = q3 - q1 
    lower = q1 - 1.5 * iqr
    upper = q3 + 1.5 * iqr
    data = data [(data[i] >= lower) & (data[i ] <= upper)]

for col in data:
    plt.figure(figsize=(6, 2))
    sns.boxplot(x=data[col], color="green")
    plt.title(f"Boxplot of {col}")
    plt.tight_layout()
    plt.show()

from sklearn.model_selection import train_test_split

x = data.drop("diagnosis",axis=1)
y = data.diagnosis

x_train,x_test,y_train,y_test = train_test_split(x ,y, test_size=0.2 , random_state=10)

y.value_counts()

diagnosis
0    219
1     14
Name: count, dtype: int64

target_var = y.map({0: "Benign", 1: "Malignant"}).value_counts()

plt.bar(target_var.index, target_var.values, color="red")
plt.xlabel("Diagnosis")
plt.ylabel("Count")
plt.title("Diagnosis Distribution")
plt.show()

from sklearn.preprocessing import StandardScaler

scaler = StandardScaler()
x_train = scaler.fit_transform(x_train)
x_test = scaler.transform(x_test)

from sklearn.linear_model import LogisticRegression

model = LogisticRegression()
model.fit(x_train,y_train)

LogisticRegression()

LogisticRegression()

y_pred = model.predict(x_test)
y_pred

array([0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0,
       0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 1, 0, 0, 0, 0,
       1, 0, 0])

from sklearn.metrics import confusion_matrix , precision_score ,auc, roc_curve,roc_auc_score,recall_score,classification_report ,accuracy_score

# Accuracy
accuracy = accuracy_score(y_test,y_pred)
print("\nAcuuracy score is :\n",accuracy)

#confusion_matrix
cm = confusion_matrix(y_test, y_pred)
print("\nConfusion Matrix is:\n", cm)

#precision
precision = precision_score(y_test, y_pred)  # use 'M' if diagnosis is 'M' or 'B'
print("\nPrecision score is :", precision)

# Recall
recall = recall_score(y_test, y_pred)
print("\nRecall score is :", recall)


# ROC AUC Score
y_proba = model.predict_proba(x_test)[:, 1] 
roc_auc = roc_auc_score(y_test, y_proba)
print("\nROC AUC Score is:", roc_auc)

# clasification report
cr = classification_report(y_test, y_pred)
print("\n classification report is :\n", cr)

Acuuracy score is :
 0.9574468085106383

Confusion Matrix is:
 [[43  0]
 [ 2  2]]

Precision score is : 1.0

Recall score is : 0.5

ROC AUC Score is: 0.9941860465116279

 classification report is :
               precision    recall  f1-score   support

           0       0.96      1.00      0.98        43
           1       1.00      0.50      0.67         4

    accuracy                           0.96        47
   macro avg       0.98      0.75      0.82        47
weighted avg       0.96      0.96      0.95        47

cm = confusion_matrix(y_test, y_pred)

sns.heatmap(cm , annot=True  ,cmap="coolwarm", fmt=".2f")
plt.title("Confusion Metrix")
plt.tight_layout()
plt.show()

y_proba = model.predict_proba(x_test)[:, 1]

fpr, tpr, thresholds = roc_curve(y_test, y_proba)
roc_auc = auc(fpr, tpr)

plt.figure(figsize=(8, 6))
plt.plot(fpr, tpr, color='darkorange', label=f'ROC Curve (AUC = {roc_auc:.2f})')
plt.plot([0, 1], [0, 1], color='navy', linestyle='--') 

plt.xlabel('False Positive Rate')
plt.ylabel('True Positive Rate')
plt.title('ROC Curve')
plt.legend(loc="lower right")
plt.show()

	id	diagnosis	radius_mean	texture_mean	perimeter_mean	area_mean	smoothness_mean	compactness_mean	concavity_mean	concave points_mean	...	radius_worst	texture_worst	perimeter_worst	area_worst	smoothness_worst	compactness_worst	concavity_worst	concave points_worst	symmetry_worst	fractal_dimension_worst
19	8510426	0	13.540	14.36	87.46	566.3	0.09779	0.08129	0.06664	0.04781	...	15.11	19.26	99.70	711.2	0.14400	0.17730	0.23900	0.12880	0.2977	0.07259
20	8510653	0	13.080	15.71	85.63	520.0	0.10750	0.12700	0.04568	0.03110	...	14.50	20.49	96.09	630.5	0.13120	0.27760	0.18900	0.07283	0.3184	0.08183
21	8510824	0	9.504	12.44	60.34	273.9	0.10240	0.06492	0.02956	0.02076	...	10.23	15.66	65.13	314.9	0.13240	0.11480	0.08867	0.06227	0.2450	0.07773
37	854941	0	13.030	18.42	82.61	523.8	0.08983	0.03766	0.02562	0.02923	...	13.30	22.81	84.46	545.9	0.09701	0.04619	0.04833	0.05013	0.1987	0.06169
40	855167	1	13.440	21.58	86.18	563.0	0.08162	0.06031	0.03110	0.02031	...	15.93	30.25	102.50	787.9	0.10940	0.20430	0.20850	0.11120	0.2994	0.07146
43	856106	1	13.280	20.28	87.32	545.2	0.10410	0.14360	0.09847	0.06158	...	17.38	28.00	113.10	907.2	0.15300	0.37240	0.36640	0.14920	0.3739	0.10270
48	857155	0	12.050	14.63	78.04	449.3	0.10310	0.09092	0.06592	0.02749	...	13.76	20.70	89.88	582.6	0.14940	0.21560	0.30500	0.06548	0.2747	0.08301
49	857156	0	13.490	22.30	86.91	561.0	0.08752	0.07698	0.04751	0.03384	...	15.15	31.82	99.00	698.8	0.11620	0.17110	0.22820	0.12820	0.2871	0.06917
50	857343	0	11.760	21.60	74.72	427.9	0.08637	0.04966	0.01657	0.01115	...	12.98	25.72	82.98	516.5	0.10850	0.08615	0.05523	0.03715	0.2433	0.06563
51	857373	0	13.640	16.34	87.21	571.8	0.07685	0.06059	0.01857	0.01723	...	14.67	23.19	96.08	656.7	0.10890	0.15820	0.10500	0.08586	0.2346	0.08025

	count	mean	std	min	25%	50%	75%	max
id	569.0	3.037183e+07	1.250206e+08	8670.000000	869218.000000	906024.000000	8.813129e+06	9.113205e+08
radius_mean	569.0	1.412729e+01	3.524049e+00	6.981000	11.700000	13.370000	1.578000e+01	2.811000e+01
texture_mean	569.0	1.928965e+01	4.301036e+00	9.710000	16.170000	18.840000	2.180000e+01	3.928000e+01
perimeter_mean	569.0	9.196903e+01	2.429898e+01	43.790000	75.170000	86.240000	1.041000e+02	1.885000e+02
area_mean	569.0	6.548891e+02	3.519141e+02	143.500000	420.300000	551.100000	7.827000e+02	2.501000e+03
smoothness_mean	569.0	9.636028e-02	1.406413e-02	0.052630	0.086370	0.095870	1.053000e-01	1.634000e-01
compactness_mean	569.0	1.043410e-01	5.281276e-02	0.019380	0.064920	0.092630	1.304000e-01	3.454000e-01
concavity_mean	569.0	8.879932e-02	7.971981e-02	0.000000	0.029560	0.061540	1.307000e-01	4.268000e-01
concave points_mean	569.0	4.891915e-02	3.880284e-02	0.000000	0.020310	0.033500	7.400000e-02	2.012000e-01
symmetry_mean	569.0	1.811619e-01	2.741428e-02	0.106000	0.161900	0.179200	1.957000e-01	3.040000e-01
fractal_dimension_mean	569.0	6.279761e-02	7.060363e-03	0.049960	0.057700	0.061540	6.612000e-02	9.744000e-02
radius_se	569.0	4.051721e-01	2.773127e-01	0.111500	0.232400	0.324200	4.789000e-01	2.873000e+00
texture_se	569.0	1.216853e+00	5.516484e-01	0.360200	0.833900	1.108000	1.474000e+00	4.885000e+00
perimeter_se	569.0	2.866059e+00	2.021855e+00	0.757000	1.606000	2.287000	3.357000e+00	2.198000e+01
area_se	569.0	4.033708e+01	4.549101e+01	6.802000	17.850000	24.530000	4.519000e+01	5.422000e+02
smoothness_se	569.0	7.040979e-03	3.002518e-03	0.001713	0.005169	0.006380	8.146000e-03	3.113000e-02
compactness_se	569.0	2.547814e-02	1.790818e-02	0.002252	0.013080	0.020450	3.245000e-02	1.354000e-01
concavity_se	569.0	3.189372e-02	3.018606e-02	0.000000	0.015090	0.025890	4.205000e-02	3.960000e-01
concave points_se	569.0	1.179614e-02	6.170285e-03	0.000000	0.007638	0.010930	1.471000e-02	5.279000e-02
symmetry_se	569.0	2.054230e-02	8.266372e-03	0.007882	0.015160	0.018730	2.348000e-02	7.895000e-02
fractal_dimension_se	569.0	3.794904e-03	2.646071e-03	0.000895	0.002248	0.003187	4.558000e-03	2.984000e-02
radius_worst	569.0	1.626919e+01	4.833242e+00	7.930000	13.010000	14.970000	1.879000e+01	3.604000e+01
texture_worst	569.0	2.567722e+01	6.146258e+00	12.020000	21.080000	25.410000	2.972000e+01	4.954000e+01
perimeter_worst	569.0	1.072612e+02	3.360254e+01	50.410000	84.110000	97.660000	1.254000e+02	2.512000e+02
area_worst	569.0	8.805831e+02	5.693570e+02	185.200000	515.300000	686.500000	1.084000e+03	4.254000e+03
smoothness_worst	569.0	1.323686e-01	2.283243e-02	0.071170	0.116600	0.131300	1.460000e-01	2.226000e-01
compactness_worst	569.0	2.542650e-01	1.573365e-01	0.027290	0.147200	0.211900	3.391000e-01	1.058000e+00
concavity_worst	569.0	2.721885e-01	2.086243e-01	0.000000	0.114500	0.226700	3.829000e-01	1.252000e+00
concave points_worst	569.0	1.146062e-01	6.573234e-02	0.000000	0.064930	0.099930	1.614000e-01	2.910000e-01
symmetry_worst	569.0	2.900756e-01	6.186747e-02	0.156500	0.250400	0.282200	3.179000e-01	6.638000e-01
fractal_dimension_worst	569.0	8.394582e-02	1.806127e-02	0.055040	0.071460	0.080040	9.208000e-02	2.075000e-01
Unnamed: 32	0.0	NaN	NaN	NaN	NaN	NaN	NaN	NaN

	count	unique	top	freq	mean	std	min	25%	50%	75%	max
id	569.0	NaN	NaN	NaN	30371831.432337	125020585.612224	8670.0	869218.0	906024.0	8813129.0	911320502.0
diagnosis	569	2	B	357	NaN	NaN	NaN	NaN	NaN	NaN	NaN
radius_mean	569.0	NaN	NaN	NaN	14.127292	3.524049	6.981	11.7	13.37	15.78	28.11
texture_mean	569.0	NaN	NaN	NaN	19.289649	4.301036	9.71	16.17	18.84	21.8	39.28
perimeter_mean	569.0	NaN	NaN	NaN	91.969033	24.298981	43.79	75.17	86.24	104.1	188.5
area_mean	569.0	NaN	NaN	NaN	654.889104	351.914129	143.5	420.3	551.1	782.7	2501.0
smoothness_mean	569.0	NaN	NaN	NaN	0.09636	0.014064	0.05263	0.08637	0.09587	0.1053	0.1634
compactness_mean	569.0	NaN	NaN	NaN	0.104341	0.052813	0.01938	0.06492	0.09263	0.1304	0.3454
concavity_mean	569.0	NaN	NaN	NaN	0.088799	0.07972	0.0	0.02956	0.06154	0.1307	0.4268
concave points_mean	569.0	NaN	NaN	NaN	0.048919	0.038803	0.0	0.02031	0.0335	0.074	0.2012
symmetry_mean	569.0	NaN	NaN	NaN	0.181162	0.027414	0.106	0.1619	0.1792	0.1957	0.304
fractal_dimension_mean	569.0	NaN	NaN	NaN	0.062798	0.00706	0.04996	0.0577	0.06154	0.06612	0.09744
radius_se	569.0	NaN	NaN	NaN	0.405172	0.277313	0.1115	0.2324	0.3242	0.4789	2.873
texture_se	569.0	NaN	NaN	NaN	1.216853	0.551648	0.3602	0.8339	1.108	1.474	4.885
perimeter_se	569.0	NaN	NaN	NaN	2.866059	2.021855	0.757	1.606	2.287	3.357	21.98
area_se	569.0	NaN	NaN	NaN	40.337079	45.491006	6.802	17.85	24.53	45.19	542.2
smoothness_se	569.0	NaN	NaN	NaN	0.007041	0.003003	0.001713	0.005169	0.00638	0.008146	0.03113
compactness_se	569.0	NaN	NaN	NaN	0.025478	0.017908	0.002252	0.01308	0.02045	0.03245	0.1354
concavity_se	569.0	NaN	NaN	NaN	0.031894	0.030186	0.0	0.01509	0.02589	0.04205	0.396
concave points_se	569.0	NaN	NaN	NaN	0.011796	0.00617	0.0	0.007638	0.01093	0.01471	0.05279
symmetry_se	569.0	NaN	NaN	NaN	0.020542	0.008266	0.007882	0.01516	0.01873	0.02348	0.07895
fractal_dimension_se	569.0	NaN	NaN	NaN	0.003795	0.002646	0.000895	0.002248	0.003187	0.004558	0.02984
radius_worst	569.0	NaN	NaN	NaN	16.26919	4.833242	7.93	13.01	14.97	18.79	36.04
texture_worst	569.0	NaN	NaN	NaN	25.677223	6.146258	12.02	21.08	25.41	29.72	49.54
perimeter_worst	569.0	NaN	NaN	NaN	107.261213	33.602542	50.41	84.11	97.66	125.4	251.2
area_worst	569.0	NaN	NaN	NaN	880.583128	569.356993	185.2	515.3	686.5	1084.0	4254.0
smoothness_worst	569.0	NaN	NaN	NaN	0.132369	0.022832	0.07117	0.1166	0.1313	0.146	0.2226
compactness_worst	569.0	NaN	NaN	NaN	0.254265	0.157336	0.02729	0.1472	0.2119	0.3391	1.058
concavity_worst	569.0	NaN	NaN	NaN	0.272188	0.208624	0.0	0.1145	0.2267	0.3829	1.252
concave points_worst	569.0	NaN	NaN	NaN	0.114606	0.065732	0.0	0.06493	0.09993	0.1614	0.291
symmetry_worst	569.0	NaN	NaN	NaN	0.290076	0.061867	0.1565	0.2504	0.2822	0.3179	0.6638
fractal_dimension_worst	569.0	NaN	NaN	NaN	0.083946	0.018061	0.05504	0.07146	0.08004	0.09208	0.2075
Unnamed: 32	0.0	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN

	id	diagnosis	radius_mean	texture_mean	perimeter_mean	area_mean	smoothness_mean	compactness_mean	concavity_mean	concave points_mean	...	radius_worst	texture_worst	perimeter_worst	area_worst	smoothness_worst	compactness_worst	concavity_worst	concave points_worst	symmetry_worst	fractal_dimension_worst
0	842302	1	17.99	10.38	122.80	1001.0	0.11840	0.27760	0.30010	0.14710	...	25.380	17.33	184.60	2019.0	0.16220	0.66560	0.7119	0.2654	0.4601	0.11890
1	842517	1	20.57	17.77	132.90	1326.0	0.08474	0.07864	0.08690	0.07017	...	24.990	23.41	158.80	1956.0	0.12380	0.18660	0.2416	0.1860	0.2750	0.08902
2	84300903	1	19.69	21.25	130.00	1203.0	0.10960	0.15990	0.19740	0.12790	...	23.570	25.53	152.50	1709.0	0.14440	0.42450	0.4504	0.2430	0.3613	0.08758
3	84348301	1	11.42	20.38	77.58	386.1	0.14250	0.28390	0.24140	0.10520	...	14.910	26.50	98.87	567.7	0.20980	0.86630	0.6869	0.2575	0.6638	0.17300
4	84358402	1	20.29	14.34	135.10	1297.0	0.10030	0.13280	0.19800	0.10430	...	22.540	16.67	152.20	1575.0	0.13740	0.20500	0.4000	0.1625	0.2364	0.07678
...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...
564	926424	1	21.56	22.39	142.00	1479.0	0.11100	0.11590	0.24390	0.13890	...	25.450	26.40	166.10	2027.0	0.14100	0.21130	0.4107	0.2216	0.2060	0.07115
565	926682	1	20.13	28.25	131.20	1261.0	0.09780	0.10340	0.14400	0.09791	...	23.690	38.25	155.00	1731.0	0.11660	0.19220	0.3215	0.1628	0.2572	0.06637
566	926954	1	16.60	28.08	108.30	858.1	0.08455	0.10230	0.09251	0.05302	...	18.980	34.12	126.70	1124.0	0.11390	0.30940	0.3403	0.1418	0.2218	0.07820
567	927241	1	20.60	29.33	140.10	1265.0	0.11780	0.27700	0.35140	0.15200	...	25.740	39.42	184.60	1821.0	0.16500	0.86810	0.9387	0.2650	0.4087	0.12400
568	92751	0	7.76	24.54	47.92	181.0	0.05263	0.04362	0.00000	0.00000	...	9.456	30.37	59.16	268.6	0.08996	0.06444	0.0000	0.0000	0.2871	0.07039

Importing Breast Cancer dataset¶

Basic understanding about Breast Cancer Wisconsin (Diagnostic) DataSet¶

Columns distribution¶

basic statistics about the Breast Cancer data :¶

Visualizations:¶

Preprocessing :¶

1. Handle null values:¶

2. Handle categorical cloumns¶

3. detect Outlines and Remove¶

Feature Engineering:¶

Feature Scaling¶

Logistic Regression model import¶

prediction¶

model Evaluation¶